Use any Whisper model on the hub for long transcription using the transformers pipeline! Just add chunk_length_s parameter to the pipeline and it'll slide through the audio to create a transcription.
CODE
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Whisper Models Hub: Demos and Scripts Showcase
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With great models, come excellent capabilities! With the influx of many brilliant models on the hub, we created some demos and scripts to showcase what the Whisper models are capable of! Here's a thread of all the cool stuff you can do with Whisper models on the Hub
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DeepONets Modification to Function-to-Function Neural Operators
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DeepONets were originally defined on a fixed input grid and hence, do not fall under operators where both inputs and outputs are functions. We can convert it into a neural operator through some modifications. See proposition 5 in https://
arxiv.org/pdf/2108.08481
.pdf
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Full Machine Learning Project Data Visualization with Matplotlib
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Full Machine Learning Project — Data Visualization with Matplotlib (Part 3) #DataScience
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Automated Image Annotation Tool Using OpenCV
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Do we need to manually annotate a dataset every time? Doesn’t it sound tedious?
— Satya Mallick (@LearnOpenCV) 8 décembre 2022
In case you missed it, check out our blog post, where we set up an automated image annotation tool using OpenCV.https://t.co/z0sye0vdZc #computervision #artificialintelligence #annotation #ai pic.twitter.com/IETjOH6jlWDo we need to manually annotate a dataset every time? Doesn’t it sound tedious?
In case you missed it, check out our blog post, where we set up an automated image annotation tool using OpenCV. https://
learnopencv.com/automated-imag
e-annotation-tool-using-opencv-python/
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FastAI Students Explain Diffusion Model Paper
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Another great diffusion model paper explainer from our wonderful @fastdotai students!
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JavaScript trademark ownership sparks concern among developers
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still not cool that they own the trademark for Javascript tho
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Zero-shot Super-resolution Tests for Neural Operators Benchmark
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But it is crucial to add zero-shot super-resolution tests. Neural operators can output at any resolution since they learn mappings between function spaces, but U-Nets cannot. Can u please add these tests to the benchmark? @rejuvyesh @jo_brandstetter
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Recent papers on LLMs for formal mathematics and theorem proving
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We had two papers on this topic recently, one using a MCTS approach on formal systems (such as Lean or Metamath): https://
arxiv.org/abs/2205.11491 and one where we combine LLMs with formal provers to verify the generations: https://
arxiv.org/abs/2210.12283 -
PDEArena Benchmarking and FNO Architectural Improvements in PyTorch
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I agree. Great to see PDEArena as a convenient benchmarking environment for PDE in @pytorchlightnin Also great to see further architectural improvements to FNO by integrating it within U-Net like structure. Would be great if PINO can also be added https://
arxiv.org/abs/2111.03794